Variant-specific inflation factors for assessing population stratification at the phenotypic variance level
Author
Sofer, TamarZheng, Xiuwen
Laurie, Cecelia A
Gogarten, Stephanie M
Brody, Jennifer A
Conomos, Matthew P
Bis, Joshua C
Thornton, Timothy A
Szpiro, Adam
O'Connell, Jeffrey R
Lange, Ethan M
Gao, Yan
Cupples, L Adrienne
Psaty, Bruce M
Rice, Kenneth M
Date
2021-06-09Journal
Nature CommunicationsPublisher
Springer NatureType
Article
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In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.Keyword
genetic association analysesphenotypic variance
population stratification
variant-specific inflation factors
Whole Genome Sequencing
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http://hdl.handle.net/10713/16016ae974a485f413a2113503eed53cd6c53
10.1038/s41467-021-23655-2
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